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Use when you have execution-time metrics (from a benchmark table or profiling logs) across multiple visualization backends for the same set of plots (e.
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| name | backend-performance-comparison |
| description | Use when you have execution-time metrics (from a benchmark table or profiling logs) across multiple visualization backends for the same set of plots (e. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3438","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3520"],"tools":["pyOpenMS-viz","Python","plotly","bokeh","matplotlib","Pandas"],"techniques":["mass-spectrometry"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.jproteome.4c00873","title":"pyopenmsviz"}] |
| evidence_spans | ["Chromatograms can be plotted using kind = chromatogram","pyOpenMS-Viz is a visualization package for mass spectrometry data directly from pandas dataframes","ms_data.plot(x=\"m/z\", y=\"intensity\", kind=\"spectrum\")","conda create --name=pyopenms-viz python=3.12","Multiple backends supported including matplotlib, bokeh, and plotly","Rendering is typically slower than the BOKEH backend"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_pyopenmsviz_cq","doi":"10.1021/acs.jproteome.4c00873","title":"pyopenmsviz"}],"dedup_kept_from":"coll_pyopenmsviz_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.jproteome.4c00873","all_source_dois":["10.1021/acs.jproteome.4c00873"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Systematically compare rendering performance across multiple plotting backends (matplotlib, bokeh, plotly) for mass spectrometry visualizations by extracting execution times from a computation-times table, aggregating them by backend and visualization type, and calculating speedup ratios. This skill identifies which backend delivers faster interactive or static rendering for specific MS data types.
You have execution-time metrics (from a benchmark table or profiling logs) across multiple visualization backends for the same set of plots (e.g., chromatogram, spectrum, peakmap, mobilogram) and need to determine which backend renders fastest for your use case, or to quantify performance trade-offs when choosing between static (matplotlib) and interactive (bokeh/plotly) backends for MS data.
Extract execution times from the computation-times table for all gallery examples, separating entries by backend (bokeh, plotly, matplotlib). Aggregate rendering times by backend and by plot category (chromatogram, spectrum, peakmap, mobilogram, etc.). For examples that exist in multiple backends, perform pairwise time comparisons and compute the mean speedup ratio (slower_backend_time / faster_backend_time). Verify consistency of the speedup across plot types; plot categories with extreme outliers should be flagged. Generate a summary table with rendering times (in milliseconds or seconds) and speedup factors. The rationale is that backend choice affects interactivity trade-offs (interactive backends like bokeh and plotly add overhead compared to static matplotlib), so rendering speed must be measured concretely for your data scale and visualization type.
import pandas as pd; times = pd.read_csv('computation_times.csv'); bokeh_times = times[times['backend']=='bokeh'].groupby('plot_type')['time_ms'].mean(); plotly_times = times[times['backend']=='plotly'].groupby('plot_type')['time_ms'].mean(); speedup = plotly_times / bokeh_times; print(speedup)
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.